Extended Kalman Filter State Estimation for Aerial Continuum Manipulation Systems
Why this work is in the frame
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Bibliographic record
Abstract
The primary goal of this letter is to address the state estimation problem for dual-arm tendon-driven aerial continuum manipulation systems (ACMSs). While the state estimation problem for conventional rigid aerial manipulation systems (AMSs) has been addressed, parameter estimation remains a significant challenge for the recently introduced ACMS platform. Compared to AMSs that utilize arms’ encoder data, ACMSs with flexible arms are not equipped with such sensors. As a result of the requirement for external sensors such as vision systems, measurement challenges may arise in ACMSs cases. Additionally, the dynamics of ACMSs are substantially more complicated, coupled, and nonlinear, posing additional barriers to tackling the estimating problem at hand. This letter proposes integrating deep neural networks with the extended Kalman filter (EKF) technique to enable real-time applications of the method. Simulation results demonstrate the performance of the suggested learning-based EKF approach.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it